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ASL Fingerspelling Recognition
Owen Mireles Briones, Tong Qi, Haidong Tian, Steven Gubkin, Brady Hood, Guillermo Castillo Martinez
The goal of this project is to detect and translate American Sign Language (ASL) fingerspelling into text. We will create a model trained on the largest dataset of its kind, released specifically for this project by Google (it is a Kaggle competition). The data includes more than three million fingerspelled characters produced by over 100 Deaf signers captured via the selfie camera of a smartphone with a variety of backgrounds and lighting conditions.
We would be entering the following Kaggle competition: https://www.kaggle.com/competitions/asl-fingerspelling/overview.
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